Few-Shot-Learning for Scar Recognition: A CNN-based Binary Classification Approach

Scar recognition is crucial issue in various medical fields, such as dermatology and plastic surgery. Conventional approaches to scar recognition often require large labeled datasets for effective training, which can be challenging to obtain due to the variability and diversity of scar patterns. In this paper, we propose a novel approach combining Convolutional Neural Networks ( CNNs) with few-shot learning techniques for scar recognition. By leveraging the feature extraction capabilities of CNNs and the generalization ability of few-shot learning from small amounts of data, this method demonstrates promising results in binary scar classification. This offers potential applicability beyond typical scars, catering to a wide range of scar types in both clinical and everyday settings. Such findings could contribute to enhancing medical efficiency in the field, aiding specialists in effectively devising personalized scar treatment plans for patients.